Using data to improve the student journey

Nick Waters, senior consultant at Barrachd, part of Capita, asks if universities are making the best use of their data to ensure a positive student experience

The UK’s higher education sector may be world renowned, but in a time of educational and economic reform, there are many new challenges to meet. With rising costs, reductions in funding, pressures for accountability and the shadow of Brexit looming large, universities must find ways to maintain and improve the experience of their students if they are to remain the envy of the education sector. Thankfully the answers are there to be found, they’re just hidden in the data universities already have…

Learner analytics

Today, students are leaving behind an increasing trail of data footprints. Enrolment data, course performance, even social media sentiment, student demographics, location data and learning systems interaction. Universities now have a wealth of information that can be used to understand how students interact and learn, and how to optimise this to enhance the student journey. Using this data to improve the student attrition can help to reduce the universal issue facing colleges and universities – that if their experience isn’t right, students will leave.

Recent HESA figures show that 26,000 students in England didn’t make it beyond their first year. This equates to 6.4% of students starting a full-time first degree course in England quitting before their second year in 2016; with almost one in five undergraduates quitting by the end of their first year at the worst affected institutions. This has an impact on the institution’s reputation – as well as its bottom line. But analytics can help decrease and manage this attrition. The first step to resolving the issue is to understand the factors that lead a student to leave. It’s generally acknowledged that a student’s inability to perform academically is a chief cause of attrition. But it’s often not just academic factors that contribute. The cost of attending university is increasing, putting huge financial burdens on students. Health and mental issues also play a part in influencing student’s decisions not to return.

Spotting early warnings

One presumption might be that students, away from home for the first time, struggle with the associated anxieties of a new living situation. However, the work we carried out revealed surprising results – amongst those most likely to drop out were those staying at home – those who failed to integrate fully into university life. And all the signals are there to see if universities have the tools to find them in time.

Our student retention model looks at student performance past and present to uncover hidden patterns in student behaviour to predict better student outcomes and protect student revenue streams. Using the data available helps higher education institutions get a comprehensive single view of each student (as well as a university-wide view), from which they can use analytics to determine who may be at risk. Once identified, the issues that could lead to student attrition can be addressed, either through additional academic counselling, financial aid support or health and mental health services.

This process isn’t easy, though. It requires aligning data from multiple, often disparate sources for that comprehensive view of the student. One must then analyse and consume that data through the application of analytics, such as dashboards, reports and predictive models to find patterns that identify those students at risk of attrition.

Artificial Intelligence

While AI may still be in its infancy, we’re already living in a world that’s infused with automation. And as machine learning techniques and natural language understanding evolve, AI is embedded in our digital lives. AI can further help universities improve their student journey, spotting hidden correlations and identifying trends before we even know which questions to ask, helping to boost the university experience for every student… and staff member!

A front-facing example of augmented intelligence is the Virtual Assistant. Already successfully employed by a handful of universities, these digital campus assistants can be designed and deployed to enhance a range of services used by students, teachers and support teams across the university. From reducing demands on labour to delivering timely, personalized and contextualised learning and assessment aids, these data-led tools enhance how students learn and engage.

Planning for the future

Of course, the student experience in general – from learning and teaching to the university estate and opportunities, all have a bearing on the decisions that students make – whether they want to stay, or go, or even join the university in the first place. This is where other analytics solutions come in. Student number planning; estate planning; financial planning, budgeting, forecasting and reporting; workforce and workload planning; and research fund planning are all key areas in which analytics can play an important role.

The student experience isn’t confined to one silo of data. Operational effectiveness across the university is vital to ensure a consistent journey for the student. And that’s why, before higher education institutions make important decisions that affect their future – and that of their students – it’s vital they attain a holistic view of their data to get the personal insight they need to ensure an excellent student journey; a journey that Capita, as leading providers of student information and analytics solutions to the higher education sector, are keen to support.

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